Treatment selection for life-critical shared decision making under ranges of health-state utility scenarios.

BACKGROUND: Selecting the best treatment for life-critical conditions via a shared decision making approach is a uniquely important challenge. Besides data from the healthcare physicians, other data that need to be considered are the personal values and perceptions of the patient. Usually, these data come in the form of health-state utility values. They are subjective and often times are elicited from the patient under emotional and stressful conditions. This paper examines an approach for selecting the best treatment under a life-critical shared decision making (SDM) framework. METHODS: Health-state utility values are used in practice to quantify what is known as quality-adjusted life years (QALYs) and quality-adjusted life expectancy (QALE). The QALEs from different treatments are used to select the best treatment. This paper describes methods for determining QALEs under a range of scenarios defined by the way some key assumptions on the health-state utility values are satisfied. Approaches for comparing different treatments are described along with some counter-intuitive results. These approaches are based on some optimization formulations. The proposed approaches are demonstrated in terms of a real example taken from the literature. RESULTS: Having results that are robust under a spectrum of different scenarios can provide more confidence that the most suitable treatment has been selected in a given case. On the other hand, having non-robust results can be useful information too as they may provide evidence that a more thorough assessment of the benefits and harms of the treatments may be needed to select a treatment with higher confidence. Finally, this study demonstrates that under certain mathematical conditions among the data it is possible to decide which treatment is better among two treatments without having to use health-state utility values. CONCLUSION: The significance of this study is that it provides valuable and actionable insights for the important question of how health-state utilities can be used in treatment selection.

The seven key challenges for life-critical shared decision making systems.

BACKGROUND: Shared decision making (SDM) for life-critical diseases or conditions is a crucial type of SDM. This type of SDM is still greatly underdeveloped and it faces a number of key challenges. The main goal of this study is to identify the challenges that impede the development and use of life-critical SDM. METHODS: This is a hybrid research and systematic / narrative review paper. Its results were derived by analyzing reviews already conducted by the authors when they were working on six recently published papers. These papers had collectively required two systematic reviews and four narrative reviews. The topics covered in the six published papers were related to computer-aided diagnosis (CAD) in medicine, the analysis of health state utilities, and the selection of the best treatment for life-critical diseases / conditions. A new narrative review was also executed to explore some new issues. RESULTS: The key challenges for life-critical SDM relate to the following aspects: The mathematical models used to make the decisions, the data used to feed these models, the role the patient plays within the SDM framework, and finally, the role healthcare professionals play along with the pertinent rules and regulations that guide the use of this type of SDM today. CONCLUSIONS: Life-critical SDM is the most important type of SDM. However, some challenges impede its successful development and use. A number of developments and enhancements need to be made urgently for this type of SDM to become widely acceptable and useful. The seven key challenges identified in this study and the suggested directions for future research offer a compelling path towards elevating life-critical SDM to the next level and do so both effectively and efficiently.

How to identify and treat data inconsistencies when eliciting health-state utility values for patient-centered decision making.

BACKGROUND: Health utilities express the perceptions patients have on the impact potential adverse events of medical treatments may have on their quality of life. Being able to accurately assess health utilities is crucial when deciding what is the best treatment when multiple and diverse treatment options exist, or when performing a cost / utility analysis. Due to the emotional and other complexities that may exist when such data are elicited, the values of the health utilities may be inaccurate and cause inconsistencies. Existing literature indicates that such inconsistencies may be very frequent. However, no method has been developed for dealing with such inconsistencies in an effective manner. METHODS: Given a set of health utilities, this paper first explores ways for determining if there are any inconsistencies in their values. It also proposes a number of quadratic optimization approaches to best estimate the actual (and hence unknown) values when a set of initial health utility values are provided by the patient and certain inconsistencies have been detected. This is achieved by readjusting the initial values in a way that is minimal and also satisfies certain consistency requirements. RESULTS: The proposed methods are applied on an illustrative example related to localized prostate cancer. Data from some published studies were used to illustrate how a set of initial values can be analyzed. This analysis aims at readjusting them in a minimal manner that would also satisfy some key numerical constraints pertinent to health utility values. CONCLUSIONS: The numerical results and the computational complexities of the proposed models indicate that the proposed approaches are practical as they involve quadratic optimization modeling. These approaches are novel as the problem of addressing numerical inconsistencies in the elicitation process of health utilities has not been addressed adequately. The approaches are also critical in shared decision making and also when performing cost / utility analyses because health utilities play a central role in determining the quality-adjusted life years when making decisions in these healthcare domains.

The seven key challenges for the future of computer-aided diagnosis in medicine.

BACKGROUND: Computer-aided diagnosis (CAD) can assist physicians in effective and efficient diagnostic decision-making. CAD systems are currently essential tools in some areas of clinical practice. In addition, it is one of the established fields of study in the interface of medicine and computer science. There are, however, still some critical challenges that CAD systems face. METHODS: This paper first describes a new literature review protocol, the Dynamic PRISMA approach based on the well-known PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) approach. This new approach enhances the traditional approach by integrating a feedback mechanism module. As a result of the literature review, this paper identifies seven major challenges that occur today in CAD and inhibit the next major developments. RESULTS: The seven challenges described in this paper involve some technical weaknesses in the interface of medicine and computer science. These challenges are related to various algorithmic limitations, the difficulty of medical professionals to adopt new systems, problems when dealing with patient data, and the lack of guidelines and standardization regarding many aspects of CAD. This paper also describes some of the recent research developments towards these challenges. CONCLUSION: If these seven key challenges are addressed properly, then the ways for dealing with them will become the R&D pillars needed to bring CAD to the next level. This would require additional well-coordinated collaboration between researchers and practitioners in the fields of medicine and computer science.

Additive Multicriteria Decision Analysis Models: Misleading Aids for Life-Critical Shared Decision Making.

Background. There is growing interest in multicriteria decision analysis (MCDA) for shared decision making (SDM). A distinguishing feature is that a preferred treatment should extend years of life and/or improve health-related quality of life (HRQL). Additive MCDA models are inadequate for the task. A plethora of MCDA models exist, each claiming that it can correctly solve real-world problems. However, most were developed in nonhealth fields and rely on additive models. This makes the problem of choosing an MCDA model as an aid for SDM a challenging and urgent one. Methods. A published 2017 MCDA of a hypothetical prostate cancer patient is used as a case in point of how not to do and how to do MCDA for SDM. We critically review it and analyze it using several additive linear MCDA models with years of life and HRQL as attributes and the linear quality-adjusted life-year (QALY) model. The following simple reasonableness test is presented for applicability of a method as an aid for SDM: Can a treatment that causes premature death trump a treatment that causes acceptable adverse effects? Results. Additive MCDA models and the linear QALY recommend significantly different alternatives. Additive MCDA models fail the proposed reasonableness test; the linear QALY model passes. Conclusions. MCDA possesses a strong craft element in addition to its technical aspects. MCDA practitioners and clinicians need to understand model limitations to choose models appropriate to the context. Additive MCDA models are inadequate for life-critical SDM. We advocate QALY models with additional research for increased realism as a tool for SDM.